Eyelid shape estimation

ABSTRACT

Systems and methods for eyelid shape estimation are disclosed. In one aspect, after receiving an eye image of an eye (e.g., from an image capture device), an eye-box is generated over an iris of the eye in the eye image. A plurality of radial lines can be generated from approximately the center of the eye-box to an upper edge or a lower edge of the eye box. Candidate points can be determined to have local maximum derivatives along the plurality of radial lines. From the candidate points, an eyelid shape curve (e.g., for an upper eyelid or a lower eyelid) can be determined by fitting a curve (e.g., a parabola or a polynomial) to the candidate points or a subset of the candidate points.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. §119(e)to U.S. Provisional Application No. 62/208,142, filed on Aug. 21, 2015,entitled “EYELID SHAPE ESTIMATION,” which is hereby incorporated byreference in its entirety.

BACKGROUND

Field

The present disclosure relates generally to systems and methods forprocessing eye imagery.

Description of the Related Art

The human iris can be used as a source of biometric information.Biometric information can provide authentication or identification of anindividual. The process of extracting biometric information, broadlycalled a biometric template, typically has many challenges.

SUMMARY

In one aspect, a method for eyelid shape estimation is disclosed. Themethod is performed under control of a hardware computer processor. Themethod comprises receiving an eye image from an image capture device;generating a shape around the iris of the eye, wherein the shape istangent to the outermost bounds of the limbic boundary of the eye;generating lines extending from the center of the shape to edges of theshape; applying an edge detection algorithm to the eye image (e.g., thelines extending from the center of the shape) to determine candidatepoints for a boundary of an eyelid and an iris of the eye; and fittingan eyelid shape curve to at least two candidate points. In anotheraspect, the method for eyelid shape estimation can be performed by ahead mounted display system. In yet another aspect, fitting the eyelidshape curve comprises sampling randomly at least two of the candidatepoints to select a plurality of candidate points for fitting; andforming a curve with the plurality of candidate points. In some aspects,the number of candidate points is at least three, at least four, atleast five, or more. The number of candidate points may depend on anumber of degrees of freedom in the candidate contour (e.g., three inthe case of a parabola), any constraints (e.g., symmetry) that apply tothe eyelid shape curve, and so forth.

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims. Neitherthis summary nor the following detailed description purports to defineor limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an example of an eye and an eye-boxhaving a plurality of radial lines that intersect the eyelids.

FIG. 2 schematically illustrates an example of an eye and an eye-boxincluding a plurality of vertical lines that intersect the eyelids.

FIG. 3 schematically illustrates an example eyelid shape estimation.

FIG. 4 is a flow diagram of an example process of eyelid shapeestimation.

FIG. 5 is a flow diagram of another example process of eyelid shapeestimation.

FIG. 6 is a flow diagram of an example process of eyelid shapeestimation that excludes potential candidate points along a pupillaryboundary or a limbic boundary as candidate points.

FIG. 7 schematically illustrates an example of a wearable display system

Throughout the drawings, reference numbers may be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate example embodiments described herein and are not intended tolimit the scope of the disclosure.

DETAILED DESCRIPTION Overview

Extracting biometric information from an eye generally includes aprocedure for the segmentation of the iris within an eye image. Irissegmentation can involve operations including locating the irisboundaries, including finding the pupillary and limbic boundaries of theiris; localizing upper or lower eyelids if they occlude the iris;detecting and excluding occlusions of eyelashes, shadows, orreflections, and so forth. For example, the eye image can be included inan image of the face or may be an image of the periocular region of theeye. To perform iris segmentation, both the pupillary boundary (theinterior boundary of the iris) and the limbic boundary (the exteriorboundary of the iris) can be identified as separate segments of imagedata. In addition to this segmentation of the iris, the portion of theiris that is occluded by the eyelids (upper or lower) can be estimated.This estimation is performed because, during normal human activity, theentire iris of a person is rarely visible. In other words, the entireiris is not generally free from occlusions of the eyelids and eyelashes.

Estimating the portion of the iris occluded by the eyelids has presentedchallenges. Embodiments of eyelid shape estimation described hereinadvantageously can be used for estimating the portion of the irisoccluded by eyelids. The eyelid shape estimation can be used, forexample, to identify biometric traits (e.g., eyelid shape), orreconstruct poses or orientations of the body or parts of a body (e.g.,an eye) from an analysis of an eye image. The eyelid shape estimationcan be used to detect a blinking eye in an eye image. Since a blinkingeye may occlude portions of the iris, detection of an eye blink, andremoval of the corresponding eye image, may improve the quality of irissegmentation and iris authentication techniques. Also, by identifying aneyelid shape of an eye in an eye image, eyelid portions of the eye inthe eye image can be removed to reduce the amount of calculations foriris segmentation techniques so that substantially only the iris portionof the eye is used for biometric authentication.

The present disclosure will describe the generation of an eye-box overan eye image including an eye. In various embodiments, the eye-box canbe placed over the portion of the eye image. For example, an eye imagecan be obtained from a still image or a video. An eye-box can begenerated over the eye image with a plurality of radial lines extendingto the edge of the eye-box, for example, from the center of eye-box. Theupper and lower edges of the eye-box can roughly trace the boundarybetween the eyelid (upper or lower respectively) with the iris. Theeye-box can be used to assist in eyelid shape estimation.

As used herein, video is used in its ordinary sense and includes, but isnot limited to, a recording of a sequence of visual images. Each imagein a video is sometimes referred to as an image frame or simply a frame.A video can include a plurality of sequential frames, either with orwithout an audio channel. A video can include a plurality of frames,which are ordered in time. Accordingly, an image in a video can bereferred to as an eye image frame or eye image.

The present disclosure will also describe examples of the estimation ofan eyelid shape from an eye image. In some implementations, using aneye-box, candidate points can be determined with an edge detector. Aniterative process can be used to randomly sample a subset of thosecandidate points to fit a curve (e.g., a parabolic line) to an estimatedeyelid shape. Of the curves generated by this iterative process, scoresfor the curves can be determined using various methods (e.g., a measureof goodness of fit). A preferred curve can be determined by assessingwhich of the curves has the highest score and/or which of the curvesexceeds a score threshold. Candidate points that are sufficiently farfrom the preferred curve (e.g., beyond a threshold distance) may beconsidered “outlier” points and excluded from subsequent fitting oranalysis. The remaining candidate points can be considered to be“inlier” points. In some embodiments, the preferred curve can berefitted using some or all points that are considered to be “inlier”points, which can provide an improved or optimal curve that fits theeyelid (e.g., by excluding outlier points that likely are notrepresentative of the position of the eyelid).

Example Eye-Box

FIG. 1 illustrates an image of an eye 100 with eyelids 104, sclera 108(the “white” of the eye), iris 112, and pupil 116. Curve 116 a shows thepupillary boundary between the pupil 116 and the iris 112, and curve 112a shows the limbic boundary between the iris 112 and the sclera 108. Theeyelids 104 include an upper eyelid 104 a and a lower eyelid 104 b.

FIG. 1 also schematically illustrates an example of an eye-box 120 overthe eye image of the eye 100 as well as a plurality of radial lines 128a. In various embodiments, the eye-box 120 can be overlaid on the eye100 in the eye image using processes such as using an image processingalgorithm that maps the eye-box 120 to particular portions of the eyeimage. Overlaying an eye-box 120 on the image of the eye 100 can also bereferred to as generating an eye-box 120 or constructing an eye-box 120.As another example, the eye-box 120 can be overlaid on a video usingprocesses such as using a video processing algorithm that can track aneye-box 120 through a plurality of sequential frames, and overlay theeye with an eye-box 120 as the video progresses through time. In someimplementations, the eye-box 120 can be overlaid on the eye 100 in theeye image after a limbic boundary 112 a or an approximation of thelimbic boundary 112 a is determined. For example, after determining thelimbic boundary 112 a, the eye-box 120 can be placed such that thelimbic boundary is inside the boundary of the eye-box 120 overlaid onthe eye image. The region of the iris 112 not covered by the eyelids 104can be inside the boundary of the eye-box 120. As another example, afterdetermining the approximation of the limbic boundary 112 a, the eye-box120 can be placed such that the approximation of the limbic boundary isinside the boundary of the eye-box 120 overlaid on the eye image. Theregion of the iris 112 not covered by the eyelids 104 or a majority ofthe region of the iris 112 not covered by the eyelids 104 can be insidethe boundary of the eye-box 120.

The eye-box 120 can be rectangular in shape. In some implementations,the eye-box 120 can be sized so as to be a minimum size bounding boxthat includes the entire iris 112. For example, the eye-box 120 can beshaped so that vertical edges 124 a 1, 124 a 2 of the eye-box 120 aretangent to the outermost portions of the limbic boundary 112 a of theiris 112. The eye-box 120 can be shaped so that the horizontal edge 124b 1 (or 124 b 2) of the eye-box 120 extends substantially outside theboundary of the upper eyelid 104 a (or the lower eyelid 104 b) and iris112. That is, the horizontal edge 124 b 1 (or 124 b 2) can intersectthat boundary. But, as depicted in FIG. 1, the horizontal edge 124 b 1(or 124 b 2) needs not intersect the boundary at any point. Similarly,the eye-box 120 can be shaped so that the horizontal edge 124 b 1 (or124 b 2) extends beyond the boundary of the upper eyelid 104 a (or thelower eyelid 104 b) and the iris 112, at any intersecting point alongthat boundary. Accordingly, the shape of the eye-box 120 can changebased on the edge of upper eyelid 104 a or lower eyelid 104 b that isoccluding the iris 112. In some implementations, the eye-box 120 iscropped to be square. In other implementations, in which the perspectiveof the iris is deterministic, the eye-box may be shaped as aparallelogram. For example, a parallelogram can be determined from aperspective transformation applied to a square eye-box located in theplane of the iris.

With continuing reference to FIG. 1, a plurality of radial lines 128 acan be generated from the pupil 116 (e.g., emanating from the center oraround the center of the pupil 116) towards the top edge 124 b 1 of theeye-box 120 (for the upper eyelid 104 a) or towards the bottom edge 124b 2 of the eye-box 120 (for the lower eyelid 104 b). Generating theplurality of radial lines 128 a can also be referred to as constructingthe plurality of radial lines 128 a. A radial line 128 a can be a linefrom the first pixel of the horizontal edge 124 b 1 closer to the uppereyelid 104 a to the last pixel of the horizontal edge 124 b 2 closer tothe lower eyelid 104 b. A radial line 128 a can be a line from thesecond pixel of the horizontal edge 124 b 1 closer to the upper eyelid104 a to the n−1 pixel of the horizontal edge 124 b 2 closer to thelower eyelid 104 b, where n denotes the width of the eye-box 120.

The number of radial lines 128 a (for each eyelid) may be as many as thewidth of the image, measured in pixels, or it may be a subsampling ofthis width. For example, a sampling process can be used to selectcertain radial lines to be used for eyelid shape estimation so thatsufficient lines cross the eyelids to provide a good fit to the eyelidshape. The sampling process can sample according to a width pixel (e.g.,2, 5, 10, or more pixels) threshold that allows the eyelid shape to beestimated within a certain error threshold. Although seven radial linesare depicted in FIG. 1, this is for illustration, and in otherimplementations, any appropriate number of radial lines 128 a can beutilized within the eye-box 120. For example, in one embodiment, thenumber of radial lines 128 a can be the number of pixels in width acrossthe eye image. In other embodiments, the number of radial lines 128 acan be optimized to be the minimal number of lines that allow the eyelidshape to be estimated within a certain error threshold. In variousembodiments, 3, 5, 7, 10, 15, 20, 30, or more lines can be used. Thenumber of lines can be representative of the typical angular rangesubtended by an eyelid in an eye image, which is typically less than 180degrees (for each eyelid).

FIG. 2 schematically illustrates an example of an eye 100 and an eye-box120 including a plurality of vertical lines 128 b that intersect theeyelids 104. The plurality of vertical lines 128 b can be used in placeof or in addition to the plurality of radial lines 128 a. The pluralityof vertical lines 128 a can be generated that are parallel to thevertical edges 124 a 1, 124 a 2 of the eye-box 120, emanating from ahorizontal bisector line 132 of the pupil 116. In such a case, thenumber of lines (for each eyelid 104) can be as many as the width (interms of pixels or any other suitable measurement such as millimeter) ofthe image. The number of lines can also be a subsampling of the width ofthe eye image.

In other implementations, a line with any shape that extends generallyoutward from the pupil 116 (or the horizontal bisector line 132) to meetor cross the eyelids 104 can be used. Thus, a line needs not be astraight line (radial or vertical) but can be curved or have anysuitable shape.

As depicted, the eye-box 120 illustrated in FIG. 1 is rectangular inshape. However, in some implementations, the eye-box 120 can have shapessuch as polygonal or geometrical shapes (e.g., circles, ovals, etc.)generated around the boundary of the iris 112. For example, a hexagonalshape can be used for the eye-box 120. Accordingly, “eye-box” can referto any polygonal shape or geometric shape generated around the eye 100so as to include the iris 112. In such a case, a plurality of lines thatcan be considered analogous to the plurality of radial lines 128 a inFIG. 1 or the plurality of vertical lines 128 b in FIG. 2 can begenerated from the center or around the center of that polygonal shapeto the edges of that polygonal shape.

Example Eyelid Shape Estimation

FIG. 3 schematically illustrates an example of eyelid shape estimation.In various embodiments, eyelid shape estimation can also be referred toas eyelid shape determination. An example of eyelid shape determinationwill be discussed using the rectangular eye-box 120 and the plurality ofradial lines 128 a shown in FIG. 1. However, the technique can beperformed using a non-rectangular eye-box 120 such as a hexagonaleye-box 120. And the technique can be performed using the plurality ofvertical lines 128 b shown in FIG. 2 or any shape of line.

A candidate point 136 a (for estimating the shape of the upper eyelid104 a) or a candidate point 136 b (for estimating the shape of the lowereyelid 104 b) can be computed for each radial line 128 a. A candidatepoint 136 a or 136 b can be a candidate for being a point along aportion of the edge of the eyelids 104 a or 104 b. For example, thecandidate point 136 a can be a candidate for estimating the shape of theupper eyelid 104 a. Similarly, the candidate point 136 b can be acandidate for estimating the shape of the lower eyelid 104 b.

To determine these candidate points, for each radial line 128 a, thepoint at which the maximum derivative is identified can be along thedirection of the radial line 128 a. In some implementations, for eachvertical line 128 b, the point at which the maximum derivative isidentified can be along the direction of the vertical direction 128 b.The maximum derivative can be used to find edges in the eye image, wherethere is a large change in image intensity, color, luminance, etc. Agiven line may have several points where the derivatives are large,e.g., the pupil boundary 116 a, the limbic boundary 112 a, and theeyelid 104 a or 104 b. A candidate point 136 a or 136 b can be selectedas the point with a large derivative value (e.g., local maximum) andwhich has the largest distance from the center of the pupil 116 (or fromthe bisector line 132). The derivative can be determined using numericalmethods. For example, in one embodiment, a Sobel derivative operator (ora linear combination thereof) is used to determine the derivative alongthe radial line 128 a. As yet another example, a Gabor filterconvolution can be used to determine a derivative for the line 128 a. Anedge detection algorithm (e.g., a Canny edge detector) can be used toidentify the candidate points 136 a or 136 b for the eyelids (or for thepupillary and limbic boundaries).

In various embodiments, determining the maximum derivative can be viewedas applying an image processing filter to the plurality of radial lines128 a. For example, the filter can be represented as a discretedifferentiation operator. This filter can be convolved with the eyeimage to generate an image processing result comprising the maximumderivatives (or approximations thereof). Accordingly, in one embodiment,the filter can be the Sobel filter operating using two 3×3 kernels thatconvolve the eye image to generate a derivative approximation result intwo dimensions. Such a derivative approximation can be expressed as amagnitude and a direction. Points along the radial lines 128 a orvertical lines 128 b that have a locally maximum derivative magnitudecan be selected as the candidate points 136 a or 136 b for the eyelids(e.g., after excluding points that may represent pupillary boundaries).Accordingly, FIG. 3 illustrates the detected candidate points 136 a forthe upper eyelid 104 a and the detected candidate points 136 b for thelower eyelid 104 b.

In a non-limiting example implementation of estimating an eyelid shape,the eyelid shape can be represented by a conic form such as a parabola.As discussed below, other implementations are possible. The followingfitting process described herein is illustrated with reference to aparabola fitting a conic form of candidate points so that the parabolarepresents an eyelid shape. This is for illustration and is not intendedto be limiting. In other implementations, any suitable mathematicalformulation or curve can be used during the fitting procedure. Forexample, a curve can be a non-linear mathematical expression. Differentformulations or curves can be used for eyelids 104 a or 104 b.

Continuing in the example implementation of estimating the eyelid shapeillustrated in FIG. 3, a parabola can be fitted to pass through three(or more) candidate points 136 a or 136 b. For example, a random subsetof three candidate points 136 a or 136 b is drawn from the list ofcandidate points 136 a (for the upper eyelid 104 a) or 136 b (for thelower eyelid 104 b). The random subset can be selected via a randomsampling process or method. A parabolic fit curve 140 a can be madeusing the candidate points 136 a in the random subset. In someimplementations, multiple parabolic fit curves can be determined. Asillustrated, a parabolic fit curve 140 b shows another fit of the uppereyelid 104 a using a different subset of the candidate points 136 a. Aparabolic fit curve 140 c or 140 d shows a fit of the lower eyelid 104 busing a random subset of the candidate points 136 b.

To determine a parabolic fit curve 104 a for the candidate points 136 a,a random sampling of subsets of candidate points 136 a, as describedabove, can be repeated for a predetermined or fixed number of iterationsto determine candidate points 136 a for fitting the parabolic line 140a. In other embodiments, random subset sampling can be repeated until aparabolic fit curve 140 a with more than a minimum number of inliercandidate points on (or sufficiently close to) that parabolic fit curve140 a is found. For example, a candidate point can be identified as aninlier if it is located within a threshold distance of the parabolic fitcurve. As but one example, that distance can be in the range of 0.5 to2.0 pixels. Different threshold distances are possible. In variousembodiments, a combination of the above (e.g., fixed number ofiterations and a minimum number of inlier candidate points) can be usedduring the random subset sampling of candidate points 136 a or 136 b.

In some embodiments, the parabolic fit curves 140 a-140 d are scored andcompared to a score threshold. The score threshold can indicate anaccurate estimation of the eyelid 104 shape within a certain errorthreshold. The Sobel derivative can be used to score the parabolic fitcurves 140 a-140 d. For example, the parabolic fit curve 140 a can bescored by summing the Sobel derivative along each candidate point 136 aalong the parabolic fit curve 140 a. As another example of scoring, theparabolic fit curve 140 a can be scored by counting the number ofcandidate points in total which are intersected by (or sufficientlyclose to, e.g., within a number of pixels) the parabolic fit curve 140a.

The scores for the parabolic fit curves 140 a-140 d can be used toidentify a preferred parabolic line for the upper eyelid 104 a or thelower eyelid 104 b. For example, the parabolic fit curve with thehighest score can be used to determine the preferred parabolic fit curvefor that eyelid 104 a or 104 b. For example, if the parabolic fit curve140 a has a higher score than the parabolic fit curve 140 b, theparabolic fit curve 140 a can be determined to be the preferredparabolic fit of the upper eyelid 104 a.

When the preferred parabolic fit curve is determined (e.g., with thehighest score), the parabolic fitting can be repeated using not only theoriginal subset of candidate points 136 a or 136 b or inlier points thatare used to determine the preferred parabolic line, but some or all ofthe other candidate points 136 a or 136 b or inlier points. Such arefitting process may provide a more accurate estimation of an eyelidshape as compared to the actual measurement of the eyelid. On completionof this refitting process, the preferred parabolic fit curve can bedetermined to be most representative of the eyelid boundary and selectedas that eyelid boundary.

During the fitting process described herein, a fit to a random subset ofcandidate points or inlier points may result in a line that is curved inthe wrong direction for a particular eyelid. For example, an uppereyelid generally is curved downwards and a lower eyelid is generallycurved upwards. If a fit line has the wrong curvature for a particulareyelid (e.g., an upward curvature for an upper eyelid or a downwardcurvature for a lower eyelid), the fit line can be rejected from thescoring process, thereby saving processing resources and improvingefficiency of the process.

Accordingly, in some embodiments, a fit line can be rejected based onthe sign of the curvature of the fit line; with positive curvaturesbeing rejected for the upper eyelid 104 a and negative curvatures beingrejected for lower eyelid 104 b. In various implementations, thecurvature of the fit line is determined as part of the fitting process(e.g., a particular fitting coefficient may be representative of thecurvature), or the curvature of the fit line can be determined by takingthe second derivative of the function represented by fit line.

In some implementations, the eye image can optionally be pre-processedwith a filter to remove high-frequency noise from the image. The filtercan be a low-pass filter or a morphological filter such as an openfilter. The filter can remove high-frequency noise from the limbicboundary, thereby removing noise that can hinder eyelid shapeestimation.

Although the foregoing examples have been described in the context offitting a parabola to an eyelid, this is for illustration and is notintended to be limiting. In other implementations, any suitablefunctional form for an eyelid shape can be used during the fittingprocedure. The functional form for an upper eyelid 104 a may, but neednot, be different from the functional form for a lower eyelid 104 b. Thefunctional form for an eyelid can be a conic form (which includes aparabola as a particular case), a polynomial (e.g., with degree higherthan two which is representative of the conic form), a spline, arational function, or any other appropriate function. Further, in otherimplementations, the eyelid shape estimation technique may use a RandomSample Consensus (RANSAC) algorithm for fitting the candidate points 136a or 136 b to the functional form for the eyelid 104 a or 104 b. Theeyelid shape estimation technique may use other statistical oroptimization algorithms to fit the eyelid shape to the candidate points136 a or 136 b.

Example Eyelid Shape Estimation Process

FIG. 4 is a flow diagram of an example process 400 of eyelid shapeestimation. The process 400 can be implemented by a hardware processor,for example a hardware process of an augmented reality device. Theprocess 400 begins at block 404. At block 408, an eye image is received.The eye image can be received from a variety of sources including animage capture device, a head mounted display system, a server, anon-transitory computer-readable medium, or a client computing device(e.g., a smartphone). The eye image can include eyelids 104, sclera 108(the “white” of the eye), iris 112, and pupil 116 of an eye 100.

At block 412, an eye-box 120 can be generated over the eye 100 in theeye image. The eye-box 120 can be generated over the eye 100 in the eyeimage by overlaying the eye-box 120 over the eye 100 in the eye image.The eye-box 120 can be overlaid on the eye image by mapping the eye-box120 to particular portions of the eye image. In some implementations,the eye-box 120 can be overlaid on the eye image computationally by adisplay system such as a head mounted display system. The eye-box 120can be overlaid on the eye 100 in the eye image after a limbic boundary112 a or an approximation of the limbic boundary 112 a is determined.For example, after determining the limbic boundary 112 a, the eye-box120 can be overlaid such that the limbic boundary 112 a is inside theboundary of the eye-box 120 overlaid on the eye image. The region of theiris 112 not covered by the eyelids 104 can be inside the boundary ofthe eye-box 120. In some implementations, block 412 is optional, becausethe eye image may include only the particular portions of the eye 100used for eyelid shape estimation. In some such implementations, one ormore edges of the eye image function similarly to respective edges of aneye-box 120.

The eye-box 120 can be rectangular in shape. The eye-box 120 can besized so as to be a minimum size bounding box that includes the entireiris 112. For example, the eye-box 120 can be shaped so that verticaledges 124 a 1, 124 a 2 of the eye-box 120 in FIGS. 1-2 are tangent tothe outermost portions of the limbic boundary 112 a of the iris 112. Theeye-box 120 can be shaped so that the horizontal edge 124 b 1 (or 124 b2) of the eye-box 120 extends substantially outside the boundary of theupper eyelid 104 a (or the lower eyelid 104 b) and iris 112 in FIGS.1-2. Accordingly, the shape of the eye-box 120 can change based on theedge of the upper eyelid 104 a or lower eyelid 104 b that is occludingthe iris 112.

In some embodiments, block 412 can be optional. The eye image receivedcan include only portions of the eye 100. For example, the eye image caninclude portions of the eyelids 104, the sclera 108, the iris 112, andpupil 116 of the eye 100. As another example, the eye-box 120 caninclude the region of the iris 112 not covered by the eyelids 104. Ifthe limbic boundary 112 a or a portion of the limbic boundary 112 a isinside the boundary of the eye-box 120, generating an eye-box 120 overthe eye 100 in the eye image may be optional. Edges of the eye image canbe considered as edges of the eye-box 120. The eye image received can beconsidered to be inside an eye-box 120 with edges corresponding to edgesof the eye image.

At block 416, a plurality of radial lines 128 a can be generated fromthe pupil 116 (e.g., emanating from the center or around the center ofthe pupil 116) towards the top edge 124 b 1 of the eye-box 120 (for theupper eyelid 104 a) or towards the bottom edge 124 b 2 of the eye-box120 (for the lower eyelid 104 b). A radial line 128 a can be a line fromthe first pixel of the horizontal edge 124 b 1 closer to the uppereyelid 104 a to the last pixel of the horizontal edge 124 b 2 closer tothe lower eyelid 104 b. A radial line 128 a can be a line from thesecond pixel of the horizontal edge 124 b 1 closer to the upper eyelid104 a to the n−1 pixel of the horizontal edge 124 b 2 closer to thelower eyelid 104 b, where n denotes the width of the eye-box 120.

The number of radial lines 128 a (for each eyelid) may be as many as thewidth of the image, measured in pixels, or it may be a subsampling ofthis width. For example, a sampling process can be used to selectcertain radial lines to be used for eyelid shape estimation so thatsufficient lines cross the eyelids to provide a good fit to the eyelidshape. In other embodiments, the number of radial lines 128 a can beoptimized to be the minimal number of lines (e.g., three) that allow theeyelid shape to be estimated within a certain error threshold. Thenumber of lines can be representative of the typical angular rangesubtended by an eyelid in an eye image, which is typically less than 180degrees (for each eyelid).

At block 420, candidate points 136 a or 136 b can be determined for theplurality of radial lines 128 a. The candidate points 136 a or 136 b canbe determined using, for example, edge detection. Edge detection can beapplied by various edge detectors, edge detection algorithms, orfilters. For example, a Canny Edge detector can be applied to the imageto detect edges in lines of the image. Edges are points located along aline that correspond to the local maximum derivatives. The detectedpoints can be referred to as candidate points. The edge detector canalso detect and exclude points of non-interest from the candidatepoints. For example, points along the pupillary boundary 116 a or thelimbic boundary 112 a can be excluded from the candidate points.Optionally, before applying edge detection, filters can be applied tothe eye image to filter high-frequency noise. For example, amorphological open filter can be applied to the eye image.

At block 424, an eyelid shape curve is fitted to the candidate points.The eyelid shape curve can be a parabolic fit curve 140 a that isparabolic in shape. A parabola can be fitted to pass through three (ormore) candidate points 136 a. For example, a random subset of threecandidate points 136 a is drawn from the list of candidate points 136 a.The random subset can be selected via a random sampling process ormethod. A parabolic fit 140 a can be made using the candidate points inthe random subset. A parabolic fit curve 140 c shows a fit of the lowereyelid 104 b using a random subset of the candidate points 136 b.

Optionally, at decision block 428, whether the eyelid shape curve is apreferred eyelid shape curve is determined. For example, the eyelidshape curve can be scored and compared to a score threshold. The scorethreshold can indicate an accurate estimation of the eyelid 104 shapewithin a certain error threshold. The Sobel derivative can be used toscore the eyelid shape curve. For example, the eyelid shape curve can bescored by summing the Sobel derivative along each candidate point 136 aor 136 b along the eyelid shape curve. As another example of scoring,the eyelid shape curve can be scored by counting the number of candidatepoints 136 a or 136 b in total which are intersected by (or sufficientlyclose to, e.g., within a number of pixels) the eyelid shape curve. Ifthe eyelid shape curve determined at block 424 is not a preferred eyelidshape curve, the block 424 is repeated. If the eyelid shape curvedetermined at block 4224 is a preferred eyelid shape curve, the process400 proceeds to block 432.

At block 432, when the preferred eyelid shape curve is determined, thefitting of the eyelid shape curve can optionally be repeated usinginlier points. Such a refitting process may provide a more accurateestimation of an eyelid shape as compared to the actual measurement ofthe eyelid 104. On completion of this refitting process, the eyelidshape curve fitted by the refitting process can be determined to be mostrepresentative of the eyelid boundary and selected as that eyelidboundary. Thereafter, at block 436, the process 400 ends.

FIG. 5 is a flow diagram of another example process 500 of eyelid shapeestimation. The process 500 may be implemented by a hardware processor.The process 500 begins at block 504. At block 508, an eye image isreceived. The eye image can be received from a variety of sourcesincluding, but not limited to: an image capture device, a head mounteddisplay system, a server, a non-transitory computer-readable medium, ora client computing device (e.g., a smartphone).

At block 520, edge detection can be applied to the eye image todetermine candidate points 136 a or 136 b. Edge detection can be appliedby various edge detectors, edge detection algorithms, or filters. Forexample, a Canny Edge detector can be applied to the image to detectedges in lines of the image. Edges can be points located along a linethat correspond to the local maximum derivatives. The detected pointscan be referred to as candidate points 136 a or 136 b. The edge detectorcan also detect and exclude points of non-interest from the candidatepoints. For example, points along a pupillary boundary 116 a or a limbicboundary 112 a can be excluded from the candidate points 136 a or 136 b.Optionally, before applying edge detection, filters can be applied tothe eye image to filter high-frequency noise. For example, amorphological filter can be applied to the eye image.

At block 524, an eyelid shape curve 140 a-140 d can be fitted to thecandidate points 136 a or 136 b. In various implementations, the eyelidshape curve 140 a-140 d can be fitted in accordance with various fittingprocesses as described above with reference to FIG. 3. Thereafter, atblock 536, the process 500 ends.

In various embodiments, the processes 400 or 500 may be performed by ahardware processor of a head mounted display system. In otherembodiments, a remote computing device with computer-executableinstructions can cause the head mounted display system to perform theprocesses 400 or 500. In some embodiments of the processes 400 or 500,elements may occur in sequences other than as described above. Oneskilled in the art will appreciate that additional variations arepossible and within the scope of the present disclosure.

Example Eyelid Shape Estimation Process Excluding Potential CandidatePoints Along a Pupillary Boundary or a Limbic Boundary as CandidatePoints

In some embodiments, it may be advantageous to exclude potentialcandidate points along a pupillary boundary 116 a or a limbic boundary112 a as candidate points 136 a or 136 b. Potential candidate points onor within a threshold distance (e.g., 2 pixels) of the pupillaryboundary 116 a or the limbic boundary 112 a can be excluded as candidatepoints 136 a or 136 b. The remaining candidate points 136 a or 136 b canbe used to determine the upper eyelid shape or the lower eyelid shape.If candidate points 136 a or 136 b used to determine an eyelid shapeinclude points along the pupillary boundary 116 a or the limbic boundary112 a, the eyelid shape determined may not be accurately. Furthermore,to determine an accurate eyelid shape, for example an eyelid shape abovea score threshold, the candidate points may have to be sampled multipletimes to determine multiple possible eyelid shape curves for an eyelid.Determining multiple possible eyelid shape curves for an eyelid requiresmore calculations and can be less efficient.

FIG. 6 is a flow diagram of an example process 600 of eyelid shapeestimation that excludes potential candidate points along a pupillaryboundary or a limbic boundary as candidate points. The process 600starts at 604. At block 608, an eye image is received. The eye image canbe received from an image capture device, a head mounted display system,a server, a non-transitory computer-readable medium, or a clientcomputing device (e.g., a smartphone). A limbic boundary 112 a in theeye image or a pupillary boundary 116 a in the eye image can be receivedor determined at block 608. At block 610, the eye image can beoptionally processed with a filter to remove high frequency noise (e.g.,a morphological open filter).

At block 612, an eye-box 120 can be overlaid on the eye image such thatthe iris 112 (e.g., represented by the area between the limbic boundary112 a and the pupillary boundary 116 a) is inside the boundary of theeye-box 120. In some implementations, the eyelids 104 or a part of theeyelids 104 can also be inside the boundary of the eye-box 120. The eyeimage can be cropped to have a minimal image size such that the iris 112is inside the boundary of the eye-box. The shape of the eye-box 120 canbe different in different implementations. For example, the eye-box 120can be rectangular in shape. As with block 412 of the process 400, block612 may be optional.

At block 614, edges in the eye image can be detected using an edgedetector. The edges in the eye image may generally be representative ofthe pupillary boundary 116 a, the limbic boundary 112 a, and the eyelids104. The edge detector can be a Canny edge detector. The edges in theeye image can be local maximum derivatives in the eye image. In someimplementations, the local maximum derivatives can be pre-computed andstored in a look-up table.

At block 616, a plurality of radial lines 128 a or vertical lines 128 bcan be generated inside the eye-box 120. The radial lines 128 a emanatefrom the center or around the center of the pupil 116 towards the topedge 124 b 1 of the eye-box 120 and can be used to determine the uppereyelid 104 a. In some implementations, the vertical lines 128 b emanatefrom the bisector line 132 towards the top edge 124 b 1 and can be usedto determine the upper eyelid 104 a.

At block 620, candidate points 136 a can be determined for the pluralityof radial lines 128 a. A candidate point 136 a of a radial line 128 acan be a point of the radial line 128 a that intersects an edge in theeye image. The candidate point 136 a can be determined using an edgedetector. The candidate point 136 a in the eye image can have a localmaximum derivative in the eye image that can be optionally stored in alook-up table. Optionally, if a potential candidate point is along apupillary boundary 116 a or a limbic boundary 112 a, then the potentialcandidate point can be excluded from the candidate points 136 a. Thepotential candidate point can be on or within a threshold distance(e.g., 2 pixels) of the pupillary boundary 116 a or the limbic boundary112 a. The remaining candidate points 136 a can be used to determine theupper eyelid 104 a.

At block 624, a mathematical fit to the eyelid shape can be determinedfrom the candidate points 136 a. For example, RANSAC or otheroptimization or fitting algorithm can be used. In one illustrativeimplementation, for some number of iterations, the following operationsare performed. A subset of candidate points 136 a can be selected. Aneyelid shape curve can be fitted to the subset of candidate points 136 aselected. The eyelid shape curve having a parabolic shape can be aparabolic fit curve 140 a. In some implementations, the eyelid shapecurve does not have a parabolic shape. The number of candidate points136 a that intersect the eyelid shape curve (or that lie within athreshold distance of the parabolic fit curve) can be determined as ascore for the eyelid shape curve. In some implementations, the averagedistance of the candidate points 136 a to the eyelid shape curve can bedetermined as a score of the eyelid shape curve. In the next iteration,a different subset of candidate points 136 a can be used to determinethe eyelid shape curve. These operations can be repeated for a number ofiterations. The number of iterations can be a fixed number, or theoperations can be iterated until an eyelid shape curve with more than aminimum number of inlier candidate points is found. An inlier candidatepoint can be a candidate point 136 a that is within a threshold distance(e.g., a number of pixels) of the eyelid shape curve.

At block 628, the eyelid shape curve with a large score, for example thelargest score, can be selected as the initial candidate eyelid shapecurve. In some implementations, the initial candidate eyelid shape curvehas a score that is sufficiently high (e.g., above a threshold) to beconsidered to accurately represent the eyelid shape.

Optionally, at block 632, inliers to the initial eyelid shape candidatecurve can be identified. For example, inliers can include candidatepoints 136 a on or within a threshold distance (e.g., a number ofpixels) of the initial eyelid shape candidate curve. The remainingcandidate points are outliers and are discarded as not beingrepresentative of eyelid shape. A new eyelid shape curve (e.g., a newparabolic fit curve 140 a) can be determined by fitting some or all ofthe inlier points. The resulting eyelid shape curve can be consideredthe best estimate for the shape of the eyelid.

The process 600 can be repeated for the lower eyelid 104 b. For example,blocks 620, 624, 628, and 632 of the process 600 can be repeated for thelower eyelid 104 b. The order of determining the upper eyelid 104 a andthe lower eyelid 104 b can be different in different implementations.For example, the shape of the upper eyelid 104 a can be determined priorthe shape of the lower eyelid 104 b is determined. As another example,the shape of the lower eyelid 104 b can be determined prior to the shapeof the upper eyelid 104 a is determined. As yet another example, theshape of the upper eyelid 104 a and the shape of the lower eyelid 104 bcan be determined in parallel. The process 600 ends at block 636.

Example Applications of Eyelid Shape Estimation for Video Processing

Systems and methods using the eyelid shape estimation techniquesdisclosed herein can address or improve other solutions to the classicalproblems in image processing within the context of video imagery.Additionally other problems in image processing can be addressed. Forexample, the eyelid shape estimation techniques can be used for imageclassification of frames in a video (e.g., identifying the iris 112 ofthe eye), as well as for the localization of specific object typeswithin one or more frames of the video (e.g., the location of the uppereyelid 104 a). As another example, the eyelid shape estimationtechniques can be applied to a video for the application of eye-tracking(e.g., determining the orientation or direction of an eye).

In some such applications, as will be further discussed below, awearable display system can include a processor that performs eyelidshape estimation on video data acquired by an image capture deviceoperatively coupled to (e.g., attached to or included in) the wearabledisplay system. The image capture device may acquire video of thewearer's eye or other components of the wearer's body (e.g., a hand or afinger) for use in generating eye-boxes 120 or estimating eyelid shapebased on the eyelid shape estimation techniques.

The use of the eyelid shape estimation techniques advantageously permitsrecognition of eyelids in a video (e.g., acquired from an image capturedevice in a wearable display system), which may permit improvedrecognition or classification of objects in the video such as biometricinformation. For example, a conventional iris segmentation technique mayhave difficulty in determining segmentation of the eye. However, theeyelid shape estimation techniques described herein can be used tobetter distinguish between the pupillary boundary 116 a and the limbicboundary 112 a, because (generally) a portion of the iris 112 will beoccluded by an eyelid 104, whereas the pupil 116 (generally) will not beoccluded. Thus, the eyelid shape estimation techniques, as illustratedin FIGS. 1-4, can be used to recognize portions of the eye 100 that arenot occluded by the eyelid 104 and can provide for more accurate irissegmentation used in biometric extraction.

Example Wearable Display System

In some embodiments, a user device can be wearable, which mayadvantageously provide a more immersive virtual reality (VR), augmentedreality (AR), experience, wherein digitally reproduced images orportions thereof are presented to a wearer in a manner wherein they seemto be, or may be perceived as, real.

Without being limited by theory, it is believed that the human eyetypically can interpret a finite number of depth planes to provide depthperception. Consequently, a highly believable simulation of perceiveddepth may be achieved by providing, to the eye, different presentationsof an image corresponding to each of these limited number of depthplanes. For example, displays containing a stack of waveguides may beconfigured to be worn positioned in front of the eyes of a user, orviewer. The stack of waveguides may be utilized to providethree-dimensional perception to the eye/brain by using a plurality ofwaveguides to direct light from an image injection device (e.g.,discrete displays or output ends of a multiplexed display which pipeimage information via one or more optical fibers) to the viewer's eye atparticular angles (and amounts of divergence) corresponding to the depthplane associated with a particular waveguide.

In some embodiments, two stacks of waveguides, one for each eye of aviewer, may be utilized to provide different images to each eye. As oneexample, an augmented reality scene may be such that a wearer of an ARtechnology sees a real-world park-like setting featuring people, trees,buildings in the background, and a concrete platform. In addition tothese items, the wearer of the AR technology may also perceive that he“sees” a robot statue standing upon the real-world platform, and acartoon-like avatar character flying by which seems to be apersonification of a bumble bee, even though the robot statue and thebumble bee do not exist in the real world. The stack(s) of waveguidesmay be used to generate a light field corresponding to an input imageand in some implementations, the wearable display comprises a wearablelight field display. Examples of wearable display device and waveguidestacks for providing light field images are described in U.S. PatentPublication No. 2015/0016777, which is hereby incorporated by referenceherein in its entirety for all it contains.

FIG. 7 illustrates an example of a wearable display system 700 that canbe used to present a VR, AR, or MR experience to a display system weareror viewer 704. The wearable display system 700 may be programmed toperform any of the applications or embodiments described herein (e.g.,eye-box generation or eyelid shape estimation). The display system 700includes a display 708, and various mechanical and electronic modulesand systems to support the functioning of that display 708. The display708 may be coupled to a frame 712, which is wearable by the displaysystem wearer or viewer 704 and which is configured to position thedisplay 708 in front of the eyes of the wearer 704. The display 708 maybe a light field display. In some embodiments, a speaker 716 is coupledto the frame 712 and positioned adjacent the ear canal of the user insome embodiments, another speaker, not shown, is positioned adjacent theother ear canal of the user to provide for stereo/shapeable soundcontrol. The display 708 is operatively coupled 720, such as by a wiredlead or wireless connectivity, to a local data processing module 724which may be mounted in a variety of configurations, such as fixedlyattached to the frame 712, fixedly attached to a helmet or hat worn bythe user, embedded in headphones, or otherwise removably attached to theuser 704 (e.g., in a backpack-style configuration, in a belt-couplingstyle configuration).

The local processing and data module 724 may comprise a hardwareprocessor, as well as non-transitory digital memory, such asnon-volatile memory e.g., flash memory, both of which may be utilized toassist in the processing, caching, and storage of data. The data includedata (a) captured from sensors (which may be, e.g., operatively coupledto the frame 712 or otherwise attached to the wearer 704), such as imagecapture devices (such as cameras), microphones, inertial measurementunits, accelerometers, compasses, GPS units, radio devices, and/orgyros; and/or (b) acquired and/or processed using remote processingmodule 728 and/or remote data repository 732, possibly for passage tothe display 708 after such processing or retrieval. The local processingand data module 724 may be operatively coupled to the remote processingmodule 728 and remote data repository 732 by communication links 736,740, such as via a wired or wireless communication links, such thatthese remote modules 728, 732 are operatively coupled to each other andavailable as resources to the local processing and data module 724. Theimage capture device(s) can be used to capture the eye images used inthe eyelid shape estimation procedures.

In some embodiments, the remote processing module 728 may comprise oneor more processors configured to analyze and process data and/or imageinformation such as video information captured by an image capturedevice. The video data may be stored locally in the local processing anddata module 724 and/or in the remote data repository 732. In someembodiments, the remote data repository 732 may comprise a digital datastorage facility, which may be available through the internet or othernetworking configuration in a “cloud” resource configuration. In someembodiments, all data is stored and all computations are performed inthe local processing and data module 724, allowing fully autonomous usefrom a remote module.

In some implementations, the local processing and data module 724 and/orthe remote processing module 728 are programmed to perform embodimentsof generating an eye-box and estimating an eyelid shape as disclosedherein. For example, the local processing and data module 724 and/or theremote processing module 728 can be programmed to perform embodimentsdescribed herein. The local processing and data module 724 and/or theremote processing module 728 can be programmed to use the eyelid shapeestimation techniques disclosed herein in biometric extraction, forexample to identify or authenticate the identity of the wearer 704, orin eye gaze or pose estimation, for example to determine a directiontoward which each eye is looking. The image capture device can capturevideo for a particular application (e.g., video of the wearer's eye foran eye-tracking application or video of a wearer's hand or finger for agesture identification application). The video can be analyzed using theeyelid estimation techniques by one or both of the processing modules724, 728 With this analysis, processing modules 724, 728 can performeyelid shape estimation or detection and/or biometric extraction, eyepose determination, etc. In some cases, off-loading at least some of theeyelid shape estimation to a remote processing module (e.g., in the“cloud”) may improve efficiency or speed of the computations. Theparameters for eyelid shape estimation (e.g., weights, bias terms,random subset sampling factors, number, and size of filters (e.g., Sobelderivative operator), etc.) can be stored in data modules 724 and/or732.

As an illustrative example of image processing, the processing module724 can extract and formulate the biometric information from an eyeimage received from an image capture device into a numericalrepresentation. The processing module 724 can also perform numericalcalculations to represent an eye-box as a transfer function thattransforms the biometric information extracted from an eye image into animage processing result. In turn, the image processing result can beused for further processing, for example, in eyelid shape estimationmethod as described herein. The image processing result and the transferfunction can both be stored in the remote data repository 732. Whilethis illustrative example of image processing is processed with theprocessing module 724, in various implementations, the remote processingmodule 728 can perform such image processing of an eye image.Additionally, the processing modules 724, 728 can, together perform suchimage processing of an eye image.

The results of the video analysis (e.g., the estimated shape of theeyelid) can be used by one or both of the processing modules 724, 728for additional operations or processing. For example, in variousapplications, biometric identification, eye-tracking, recognition orclassification of gestures, objects, poses, etc. may be used by thewearable display system 700. For example, video of the wearer's eye(s)can be used for eyelid shape estimation, which, in turn, can be used bythe processing modules 724, 728 to determine the direction of the gazeof the wearer 704 through the display 708. The processing modules 724,728 of the wearable display system 700 can be programmed with one ormore embodiments of eye-box generation or eyelid shape estimation toperform any of the video or image processing applications describedherein.

Additional Aspects

The eyelid shape estimation techniques described herein can be appliedto an image (e.g., a video frame). Both eye-box generation and eyelidshape estimation can be viewed together as a single process and/ormethodology for processing an image of an eye.

In a 1st aspect, a method for eyelid shape estimation is disclosed. Themethod is under control of a hardware processor and comprises:generating an eye box over an eye in an eye image, wherein the eye boxis rectangular in shape with an upper edge and a lower edge, wherein theupper edge overlays a portion of an upper eyelid of the eye, and whereinthe lower edge overlays a portion of a lower eyelid of the eye; generatea plurality of radial lines extending from approximately the center ofthe eye-box to the upper edge or the lower edge, determining candidatepoints on the plurality of radial lines using an edge detector; anddetermining a parabolic fit curve from a subset of the candidate points,wherein the parabolic fit curve is an estimation of an eyelid shape ofthe eye in the eye image.

In a 2nd aspect, the method of aspect 1, wherein the eye-box is squarein shape.

In a 3rd aspect, the method of any one of aspects 1-2, furthercomprising: determining a score of the parabolic fit curve is above athreshold score; and determining that the parabolic line is a preferredestimation of the eyelid shape of the eye in the eye image.

In a 4th aspect, the method of aspect 3, wherein determining the scoreof the parabolic fit curve is above the threshold score comprisesdetermining a number of the candidate points that intersect theparabolic fit curve.

In a 5th aspect, the method of aspect 3, wherein determining the scoreof the parabolic fit curve comprises determining a number of thecandidate points that are within a threshold distance of the parabolicfit curve.

In a 6th aspect, the method of any one of aspects 1-5, whereindetermining the candidate points on the plurality of radial lines usingthe edge detector comprises determining the candidate points on a subsetof the plurality of radial lines using the edge detector.

In a 7th aspect, a head mounted display system is disclosed. The headmounted display comprises: an image capture device configured to capturean eye image of an eye; non-transitory memory; and a hardware processorin communication with the non-transitory memory, the hardware processorprogrammed to: receive the eye image from the image capture device;determine candidate points in the eye image; and determine an eyelidshape of the eye using a first subset of the candidate points.

In a 8th aspect, the head mounted display system of aspect 7, whereindetermine the candidate points in the eye image comprises: identify aportion of the eye image comprising at least a portion of a pupil of theeye and an iris of an eye.

In a 9th aspect, the head mounted display system of aspect 8, whereinidentifying the portion of the eye image comprises generating an eye-boxover the portion of the pupil and the portion of the iris in the eyeimage.

In a 10th aspect, the head mounted display system of any one of aspects7-9, wherein the non-transitory memory is configured to store an edgedetector, and wherein determine the candidate points in the eye imagecomprises: generate an eye-box around an iris of the eye, wherein afirst edge of the eye-box is tangent to a limbic boundary of the eye;generate a plurality of lines extending to a second edge of the eye-box;and determine the candidate points on the plurality of lines using theedge detector.

In a 11th aspect, the head mounted display system of aspect 10, whereingenerate the eye-box around the iris of the eye comprises overlaying aneye-box over the eye in the eye image.

In a 12th aspect, the head mounted display system of aspect 10, whereinthe plurality of lines comprises a plurality of radial lines, andwherein the plurality of radial lines extends from approximately thecenter of the eye-box to the second edge of the eye-box.

In a 13th aspect, the head mounted display system of aspect 12, whereinthe pupil of the eye is at approximately the center of the eye-box.

In a 14th aspect, the head mounted display system of any one of aspects10-13, wherein the plurality of lines comprises a plurality of verticallines, and wherein the plurality of vertical lines extends from abisector line of the eye-box to the second edge of the eye-box.

In a 15th aspect, the head mounted display system of aspect 14, whereinthe bisector lines passes through the pupil of the eye in the eye image.

In a 16th aspect, the head mounted display system of any one of aspects10-15, wherein the candidate points comprise points with local maximumderivatives along the plurality of the lines.

In a 17th aspect, the head mounted display system of any one of aspects10-16, wherein the candidate points are not along a pupillary boundaryor a limbic boundary.

In a 18th aspect, the method of any one of aspects 10-17, wherein thehardware processor is further programmed to: apply a morphologicalfilter to the eye image prior to determining the candidate points.

In a 19th aspect, the head mounted display system of any one of aspects7-18, wherein determine the eyelid shape using the first subset of thecandidate points comprises determine a functional form of the eyelidshape using the first subset of the candidate points.

In a 20th aspect, the head mounted display system of aspect 19, whereinthe functional form is a parabola or a polynomial.

In a 21st aspect, the head mounted display system of any one of aspects7-20, wherein determine the eyelid shape using the first subset of thecandidate points comprises re-determine the eyelid shape using a secondsubset of the candidate points if the determined eyelid shape has anegative curvature and the determined eyelid shape is an estimation ofthe upper eyelid or if the determined eyelid shape has a positivecurvature and the determined eyelid shape is an estimation of the lowereyelid.

In a 22nd aspect, a method for eyelid shape estimation is disclosed. Themethod is under control of a hardware processor and comprises:identifying a portion of an eye image comprising at least a portion of apupil and an iris of an eye; generating a plurality of lines extendingto an upper edge or a lower edge of the portion of the eye image;determining candidate points on the plurality of lines using an edgedetector; and determining an eyelid shape using a subset of thecandidate points.

In a 23rd aspect, the method of aspect 22, wherein determining theeyelid shape using the subset of the candidate points comprises:determining a first functional form of the eyelid shape using a firstsubset of the candidate points; determining a second functional form ofthe eyelid shape using a second subset of the candidate points;determining a score of the first functional form of the eyelid shape;determining a score of the second functional form of the eyelid shape;and determining a preferred estimation of the eyelid shape is the firstfunctional form of the eyelid shape if the score of the first functionalform is greater than the score of the second functional form, and thesecond functional form of the eyelid shape otherwise.

In a 24th aspect, the method of any one of aspects 22-23, furthercomprising: randomly sample the candidate points to generate the subsetof the candidate points.

In a 25th aspect, the method of any one of aspect 22-24, wherein theedge detector comprises a Sobel filter, and wherein determining thecandidate points on the plurality of lines using the edge detectorcomprises: convolving the Sobel filter with the eye image to generate aderivative approximation image result; and identifying the candidatepoints using the derivative approximation image result.

In a 26th aspect, the method of any one of aspects 22-25, whereinidentifying the portion of the eye image comprises generating an eye-boxover the eye in the eye image.

In a 27th aspect, a method for processing an eye image is disclosed. Themethod is performed under control of a hardware computer processor andcomprises: generating lines extending to an upper edge or a lower edgeof an eye-box bounding an iris of an eye in an eye image; selectingcandidate points of maximum derivatives for the lines; and determining abest-fit curve matching a shape of an eyelid of the eye in the eyeimage.

In a 28th aspect, the method of aspect 27, wherein the lines compriseradial lines from a center of a pupil of the eye in the eye imageextending to the upper edge or the lower edge of the eye-box boundingthe iris of the eye in the eye image, and wherein the candidate pointsare maximum parallel Sobel directional derivatives for the radial lines

In a 29th aspect, the method of any one of aspects 27-28, wherein thelines comprise vertical lines from a horizontal bisecting line through apupil of the eye in the eye image extending to the upper edge or thelower edge of the eye-box bounding the iris of the eye in the eye image,and wherein the candidate points are maximum vertical Sobel derivativesfor the vertical lines.

In a 30th aspect, the method of any one of aspects 27-29, whereindetermining the best-fit curve matching the shape of the eyelid of theeye in the eye image comprises: randomly sampling the candidate pointsto generate a subset of the candidate points; determining the best-fitcurve matching the shape of the eyelid using the subset of the candidatepoints.

In a 31st aspect, the method of any one of aspects 27-30, wherein thebest-fit curve has a maximum number of candidate points on or close tothe best-fit curve.

In a 32nd aspect, the method of aspect 31, wherein the maximum number ofcandidate points on or close to the best-fit curve is above a maximumnumber of candidate points threshold.

In a 33rd aspect, the method of any one of aspects 27-30, wherein thebest-fit curve has a maximum integrated Sobel derivative along thebest-fit curve.

In a 34th aspect, the method of aspect 33, wherein the maximumintegrated Sobel derivative along the best-fit curve is above a Sobelderivative threshold.

In a 35th aspect, the method of any one of aspects 27-34, furthercomprising applying a morphological filter before selecting thecandidate points.

In a 36th aspect, the method of aspect 35, wherein the morphologicalfilter is an open morphological filter.

In a 37th aspect, the method of any one of aspects 27-36, furthercomprising: rejecting a candidate curve of the best-fit curve having anincorrect curvature.

In a 38th aspect, the method of aspect 37, wherein the candidate curveof the best-fit curve has the incorrect curvature if the candidate curvehas a positive curvature, and wherein the eyelid is an upper eyelid.

In a 39th aspect, the method of aspect 37, wherein the candidate curveof the best-fit curve has the incorrect curvature if the candidate curvehas a negative curvature, and wherein the eyelid is a lower eyelid.

In a 40th aspect, the method of any one of aspects 27-39, furthercomprising: pre-computing the maximum derivatives for the lines; andstoring the maximum derivatives in a look-up table.

In a 41st aspect, the method of aspect 40, wherein selecting thecandidate points with the maximum derivatives for the lines comprisesselecting the candidate points using the look-up table.

CONCLUSION

Each of the processes, methods, and algorithms described herein and/ordepicted in the attached figures may be embodied in, and fully orpartially automated by, code modules executed by one or more physicalcomputing systems, hardware computer processors, application-specificcircuitry, and/or electronic hardware configured to execute specific andparticular computer instructions. For example, computing systems caninclude general purpose computers (e.g., servers) programmed withspecific computer instructions or special purpose computers, specialpurpose circuitry, and so forth. A code module may be compiled andlinked into an executable program, installed in a dynamic link library,or may be written in an interpreted programming language. In someimplementations, particular operations and methods may be performed bycircuitry that is specific to a given function.

Further, certain implementations of the functionality of the presentdisclosure are sufficiently mathematically, computationally, ortechnically complex that application-specific hardware or one or morephysical computing devices (utilizing appropriate specialized executableinstructions) may be necessary to perform the functionality, forexample, due to the volume or complexity of the calculations involved orto provide results substantially in real-time. For example, a video mayinclude many frames, with each frame having millions of pixels, andspecifically programmed computer hardware is necessary to process thevideo data to provide a desired image processing task or application ina commercially reasonable amount of time.

Code modules or any type of data may be stored on any type ofnon-transitory computer-readable medium, such as physical computerstorage including hard drives, solid state memory, random access memory(RAM), read only memory (ROM), optical disc, volatile or non-volatilestorage, combinations of the same and/or the like. The methods andmodules (or data) may also be transmitted as generated data signals(e.g., as part of a carrier wave or other analog or digital propagatedsignal) on a variety of computer-readable transmission mediums,including wireless-based and wired/cable-based mediums, and may take avariety of forms (e.g., as part of a single or multiplexed analogsignal, or as multiple discrete digital packets or frames). The resultsof the disclosed processes or process steps may be stored, persistentlyor otherwise, in any type of non-transitory, tangible computer storageor may be communicated via a computer-readable transmission medium.

Any processes, blocks, states, steps, or functionalities in flowdiagrams described herein and/or depicted in the attached figures shouldbe understood as potentially representing code modules, segments, orportions of code which include one or more executable instructions forimplementing specific functions (e.g., logical or arithmetical) or stepsin the process. The various processes, blocks, states, steps, orfunctionalities can be combined, rearranged, added to, deleted from,modified, or otherwise changed from the illustrative examples providedherein. In some embodiments, additional or different computing systemsor code modules may perform some or all of the functionalities describedherein. The methods and processes described herein are also not limitedto any particular sequence, and the blocks, steps, or states relatingthereto can be performed in other sequences that are appropriate, forexample, in serial, in parallel, or in some other manner. Tasks orevents may be added to or removed from the disclosed exampleembodiments. Moreover, the separation of various system components inthe implementations described herein is for illustrative purposes andshould not be understood as requiring such separation in allimplementations. It should be understood that the described programcomponents, methods, and systems can generally be integrated together ina single computer product or packaged into multiple computer products.Many implementation variations are possible.

The processes, methods, and systems may be implemented in a network (ordistributed) computing environment. Network environments includeenterprise-wide computer networks, intranets, local area networks (LAN),wide area networks (WAN), personal area networks (PAN), cloud computingnetworks, crowd-sourced computing networks, the Internet, and the WorldWide Web. The network may be a wired or a wireless network or any othertype of communication network.

The systems and methods of the disclosure each have several innovativeaspects, no single one of which is solely responsible or required forthe desirable attributes disclosed herein. The various features andprocesses described above may be used independently of one another, ormay be combined in various ways. All possible combinations andsubcombinations are intended to fall within the scope of thisdisclosure. Various modifications to the implementations described inthis disclosure may be readily apparent to those skilled in the art, andthe generic principles defined herein may be applied to otherimplementations without departing from the spirit or scope of thisdisclosure. Thus, the claims are not intended to be limited to theimplementations shown herein, but are to be accorded the widest scopeconsistent with this disclosure, the principles and the novel featuresdisclosed herein.

Certain features that are described in this specification in the contextof separate implementations also can be implemented in combination in asingle implementation. Conversely, various features that are describedin the context of a single implementation also can be implemented inmultiple implementations separately or in any suitable subcombination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination. No single feature orgroup of features is necessary or indispensable to each and everyembodiment.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list. In addition, thearticles “a,” “an,” and “the” as used in this application and theappended claims are to be construed to mean “one or more” or “at leastone” unless specified otherwise.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y and atleast one of Z to each be present.

Similarly, while operations may be depicted in the drawings in aparticular order, it is to be recognized that such operations need notbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Further, the drawings may schematically depict one more exampleprocesses in the form of a flowchart. However, other operations that arenot depicted can be incorporated in the example methods and processesthat are schematically illustrated. For example, one or more additionaloperations can be performed before, after, simultaneously, or betweenany of the illustrated operations. Additionally, the operations may berearranged or reordered in other implementations. In certaincircumstances, multitasking and parallel processing may be advantageous.Moreover, the separation of various system components in theimplementations described above should not be understood as requiringsuch separation in all implementations, and it should be understood thatthe described program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts. Additionally, other implementations are within the scope ofthe following claims. In some cases, the actions recited in the claimscan be performed in a different order and still achieve desirableresults.

What is claimed is:
 1. A method for eyelid shape estimation, comprising:under control of a hardware processor: generating an eye-box over an eyein an eye image, wherein the eye-box is rectangular in shape with anupper edge and a lower edge, wherein the upper edge overlays a portionof an upper eyelid of the eye, and wherein the lower edge overlays aportion of a lower eyelid of the eye; generate a plurality of radiallines extending from approximately the center of the eye-box to theupper edge or the lower edge, determining candidate points on theplurality of radial lines using an edge detector; determining aparabolic fit curve from a subset of the candidate points, wherein theparabolic fit curve is an estimation of an eyelid shape of the eye inthe eye image; determining a score of the parabolic fit curve is above athreshold score; and determining that the parabolic fit curve is apreferred estimation of the eyelid shape of the eye in the eye image. 2.The method of claim 1, wherein the eye-box is square in shape.
 3. Themethod of claim 1, wherein determining the score of the parabolic fitcurve is above the threshold score comprises determining a number of thecandidate points that intersect the parabolic fit curve.
 4. The methodof claim 1, wherein determining the score of the parabolic fit curvecomprises determining a number of the candidate points that are within athreshold distance of the parabolic fit curve.
 5. The method of claim 1,wherein determining the candidate points on the plurality of radiallines using the edge detector comprises determining the candidate pointson a subset of the plurality of radial lines using the edge detector. 6.A head mounted display system comprising: an image capture deviceconfigured to capture an eye image of an eye; non-transitory memory; anda hardware processor in communication with the non-transitory memory,the hardware processor programmed to: receive the eye image from theimage capture device; determine candidate points in the eye image; anddetermine an eyelid shape of the eye using a first subset of thecandidate points.
 7. The head mounted display system of claim 6, whereinthe non-transitory memory is configured to store an edge detector, andwherein determine the candidate points in the eye image comprises:generate an eye-box around an iris of the eye, wherein a first edge ofthe eye-box is tangent to a limbic boundary of the eye; generate aplurality of lines extending to a second edge of the eye-box; anddetermine the candidate points on the plurality of lines using the edgedetector.
 8. The head mounted display system of claim 7, wherein theplurality of lines comprises a plurality of radial lines, and whereinthe plurality of radial lines extends from approximately the center ofthe eye-box to the second edge of the eye-box.
 9. The head mounteddisplay system of claim 7, wherein the plurality of lines comprises aplurality of vertical lines, and wherein the plurality of vertical linesextends from a bisector line of the eye-box to the second edge of theeye-box.
 10. The head mounted display system of claim 7, wherein thecandidate points comprise points with local maximum derivatives alongthe plurality of the lines.
 11. The head mounted display system of claim6, wherein the candidate points are not along a pupillary boundary or alimbic boundary.
 12. The method of claim 6, wherein the hardwareprocessor is further programmed to: apply a morphological filter to theeye image prior to determining the candidate points.
 13. The headmounted display system of claim 6, wherein determine the eyelid shapeusing the first subset of the candidate points comprises determine afunctional form of the eyelid shape using the first subset of thecandidate points.
 14. The head mounted display system of claim 13,wherein the functional form is a parabola or a polynomial.
 15. The headmounted display system of claim 6, wherein determine the eyelid shapeusing the first subset of the candidate points comprises re-determinethe eyelid shape using a second subset of the candidate points if thedetermined eyelid shape has a negative curvature and the determinedeyelid shape is an estimation of the upper eyelid or if the determinedeyelid shape has a positive curvature and the determined eyelid shape isan estimation of the lower eyelid.
 16. A method for eyelid shapeestimation, the method comprising: under control of a hardwareprocessor: identifying a portion of an eye image comprising at least aportion of a pupil and an iris of an eye; generating a plurality oflines extending to an upper edge or a lower edge of the portion of theeye image; determining candidate points on the plurality of lines usingan edge detector; and determining an eyelid shape using a subset of thecandidate points.
 17. The method of claim 16, wherein determining theeyelid shape using the subset of the candidate points comprises:determining a first functional form of the eyelid shape using a firstsubset of the candidate points; determining a second functional form ofthe eyelid shape using a second subset of the candidate points;determining a score of the first functional form of the eyelid shape;determining a score of the second functional form of the eyelid shape;and determining a preferred estimation of the eyelid shape is the firstfunctional form of the eyelid shape if the score of the first functionalform is greater than the score of the second functional form, and thesecond functional form of the eyelid shape otherwise.
 18. The method ofclaim 16, further comprising: randomly sampling the candidate points togenerate the subset of the candidate points.
 19. The method of claim 16,wherein the edge detector comprises a Sobel filter, and whereindetermining the candidate points on the plurality of lines using theedge detector comprises: convolving the Sobel filter with the eye imageto generate a derivative approximation image result; and identifying thecandidate points using the derivative approximation image result. 20.The method of claim 16, wherein identifying the portion of the eye imagecomprises generating an eye-box over the eye in the eye image.